Hadoop Vs Apache Spark
All You Need to Know About Hadoop Vs Apache Spark
Over the past few years, data science has matured substantially, so there is a huge demand for different approaches to data. There are business applications where Hadoop outweighs the newcomer Spark, but Spark has its own advantages especially when it comes down to processing speed and its ease of use. This analysis examines a common set of attributes for each platform including performance, cost, its use, data processing, compatibility, and security.
One of the most important things that you need to remember about Hadoop and Spark is that their use is quite important because they are not mutually exclusive. Keep this in mind that one cannot replace another. As a matter of fact, the two are compatible with each other and that makes their pairing an extremely powerful solution for a variety of big data applications.
What is the Difference between Hadoop & Apache Spark?
Hadoop can be defined as a framework that allows for distributed processing of large data sets (big data) using simple programming models. And the best part is that Hadoop can scale from single computer systems up to thousands of commodity systems that offer substantial local storage. When it comes to big data analytics space, Hadoop, in essence, is the ubiquitous big data gorilla.
Having observed that many companies use big data sets and analytics use Hadoop. Initially, Hadoop originally was designed to searching billions of web pages and collecting their information into a database. The result of the need to search the web was Hadoop’s HDFS and its distributed processing engine, MapReduce.
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What is Apache Spark?
The Apache Spark is considered as a fast and general engine for large-scale data processing. Most importantly, Spark’s in-memory processing admits that Spark is very fast (Up to 100 times faster than Hadoop MapReduce). In addition, Spark can also perform batch processing, however, which is really beneficial at streaming workloads, interactive queries, and machine-based learning.
According to big data experts, Spark is compatible with Hadoop and its modules.
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Comparison of Hadoop and Apache Spark
Let’s compare Hadoop and Apache Spark on the basis of these following points.
There’s no arguing with the fact that Spark is faster as compared to MapReduce. The problem with comparing the two is that they have different processing speed which is majorly included in the Data Processing section. The reason behind Spark’s fast processing is that it processes everything in memory.
Spark is renowned for its excellent performance, but it’s also somewhat well known for its ease of use and that supports languages like Java, Python, and Spark SQL. There is no denying the fact that Spark SQL is very similar to SQL 92, meaning there would be no learning curve required in order to use it.
Both MapReduce and Spark are Apache projects are open source and free software products. The main difference between both of them is that MapReduce uses standard amounts of memory because its processing is disk-based, allowing a company to purchase faster disks and a lot of disk space to run MapReduce. On the other hand, Spark requires a lot of memory, but can deal with a standard amount of disk that runs at standard speeds.
unarguably, MapReduce and Spark are compatible with each other and the bottom line is that Spark shares all MapReduce’s compatibility for data sources and file formats.
Hadoop supports Kerberos authentication, which is quite difficult to manage. On the contrary, Spark’s security is a bit sparse by currently only supporting authentication via shared secret.
Apparently, using Spark would be the preferred choice for any big data application. However, that’s not the case. MapReduce has made its way into the big data market for businesses that need huge datasets. Apache Spark’s speed, agility, and ease of use will ultimately help reduce MapReduce’s low cost of operation.
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